Combinatorial approaches for inverse metabolic engineering applications

Metabolic pathway engineering: Perspectives and applications

BACKGROUND

Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering.

CONCLUSION

In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.

Rationally designed perturbation factor drives evolution in Saccharomyces cerevisiae for industrial application

Saccharomyces cerevisiae strains with favorable characteristics are preferred for application in industries. However, the current ability to reprogram a yeast cell on the genome scale is limited due to the complexity of yeast ploids. In this study, a method named genome replication engineering-assisted continuous evolution (GREACE) was proved efficient in engineering S. cerevisiae with different ploids. Through iterative cycles of culture coupled with selection, GREACE could continuously improve the target traits of yeast by accumulating beneficial genetic modification in genome. The application of GREACE greatly improved the tolerance of yeast against acetic acid compared with their parent strain. This method could also be employed to improve yeast aroma profile and the phenotype could be stably inherited to the offspring. Therefore, GREACE method was efficient in S. cerevisiae engineering and it could be further used to evolve yeast with other specific characteristics.

Increasing Extracellular Free Fatty Acid Production in Escherichia coli by Disrupting Membrane Transport Systems

Transposon mutagenesis was used to identify three mutants of E. coli that exhibited increased free fatty acid (FFA) production, which resulted from the disruption of genes related to membrane transport. Deletion of envR, gusC, and mdlA individually in a recombinant E. coli strain resulted in 1.4-, 1.8-, and 1.2-fold increases in total FFA production, respectively. In particular, deletion of envR increased the percentage of extracellular FFA to 46%, compared with 29% for the control strain. Multiple deletion of envR, gusC, mdlA, ompF, and fadL had a synergistic effect on FFA production, resulting in high extracellular FFA production, comprising up to 50% of total FFA production. This study has identified new membrane proteins involved in FFA production and showed that genetic engineering targeting these membrane transporters is important to increase both total FFA and extracellular FFA production.

Whole cell biocatalysts: essential workers from Nature to the industry

Microorganisms have been exposed to a myriad of substrates and environmental conditions throughout evolution resulting in countless metabolites and enzymatic activities. Although mankind have been using these properties for centuries, we have only recently learned to control their production, to develop new biocatalysts with high stability and productivity and to improve their yields under new operational conditions. However, microbial cells still provide the best known environment for enzymes, preventing conformational changes in the protein structure in non-conventional medium and under harsh reaction conditions, while being able to efficiently regenerate necessary cofactors and to carry out cascades of reactions. Besides, a still unknown microbe is probably already producing a compound that will cure cancer, Alzeihmer's disease or kill the most resistant pathogen. In this review, the latest developments in screening desirable activities and improving production yields are discussed.

A metabolomics-based strategy for identification of gene targets for phenotype improvement and its application to 1-butanol tolerance in Saccharomyces cerevisiae

BACKGROUND

Traditional approaches to phenotype improvement include rational selection of genes for modification, and probability-driven processes such as laboratory evolution or random mutagenesis. A promising middle-ground approach is semi-rational engineering, where genetic modification targets are inferred from system-wide comparison of strains. Here, we have applied a metabolomics-based, semi-rational strategy of phenotype improvement to 1-butanol tolerance in Saccharomyces cerevisiae.

RESULTS

Nineteen yeast single-deletion mutant strains with varying growth rates under 1-butanol stress were subjected to non-targeted metabolome analysis by GC/MS, and a regression model was constructed using metabolite peak intensities as predictors and stress growth rates as the response. From this model, metabolites positively and negatively correlated with growth rate were identified including threonine and citric acid. Based on the assumption that these metabolites were linked to 1-butanol tolerance, new deletion strains accumulating higher threonine or lower citric acid were selected and subjected to tolerance measurement and metabolome analysis. The new strains exhibiting the predicted changes in metabolite levels also displayed significantly higher growth rate under stress over the control strain, thus validating the link between these metabolites and 1-butanol tolerance.

CONCLUSIONS

A strategy for semi-rational phenotype improvement using metabolomics was proposed and applied to the 1-butanol tolerance of S. cerevisiae. Metabolites correlated with growth rate under 1-butanol stress were identified, and new mutant strains showing higher growth rate under stress could be selected based on these metabolites. The results demonstrate the potential of metabolomics in semi-rational strain engineering.